Staff Software Engineer

---- What the Candidate Will Do ----

Strategic Leadership & Roadmap

1. Own the Technical Vision: You will own and drive the technical roadmap for the Payments data ecosystem, balancing long-term architectural scalability with short-term business critical deliveries.

2. Navigate Ambiguity: Actively identify strategically important problems and inefficiencies without waiting for instruction. You will partner with Product, Operations, and Engineering stakeholders to translate ambiguous business goals into clear, actionable technical solutions.

3. Drive Alignment: See the big picture and drive consensus on complex technical decisions across the organization. You will leverage strong relationships to align conflicting priorities and ensure multiple teams are moving in the same direction.

Technical Architecture & Execution

1. Architect at Scale: Design and implement resilient, cost-effective, and high-scale batch and streaming pipelines that power critical support operations and financial analytics.

2. Elevate Data Standards: Define and enforce robust data modeling standards, data contracts, and governance frameworks. You will lead the charge on improving data reliability, lineage, and observability to ensure trust in our data.

3. Optimize & Automate: Identify opportunities to automate manual workflows (like SLA tracking and issue detection) and optimize infrastructure efficiency to lower TCO (Total Cost of Ownership).

Culture, Mentorship & Best Practices

1. Raise the Bar: Champion sustainable engineering practices. You will be the standard-bearer for best-in-class code, documentation, testing, and monitoring, guiding the team toward the next generation of our fintech systems.

2. Be a Trusted Mentor: Serve as a humble mentor and technical advisor to both junior engineers and peer leaders. You will foster an environment of psychological safety, helping teams navigate differences in opinion to commit and move forward effectively.

3. Force Multiplier: Act as a role model for judgment and responsibility. Engineers across the organization will look to you for guidance on how to plan, execute, and deliver high-leverage solutions that impact the entire group.

---- Basic Qualifications ----

Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.

Experience Level: 10+ years of hands-on experience in Data Engineering, with a proven track record of delivering results at a Staff Engineer level (or equivalent scope) at a premier technology company.

Expert SQL Competency: 10+ years of hands-on, expert-level SQL experience. You must be able to write, optimize, and debug complex queries against massive datasets (Petabyte-scale) without relying on ORMs or abstraction layers. This includes deep knowledge of window functions, CTEs, recursive queries, and query execution plan analysis.

Data Modeling & Warehousing: Extensive experience designing dimensional data models (Star/Snowflake schemas) and data warehouses. Ability to translate complex business logic into efficient, scalable table structures that serve broad analytical needs.

Software Engineering Fundamentals: Proficiency in at least one high-level programming language (Java, Scala, Python, or Go). You will need this to build custom tooling, UDFs, and automation that extends beyond standard SQL capabilities.

Big Data Ecosystem: 10+ years of experience working with distributed data systems (Hadoop, Hive, Spark) and MPP databases (Vertica, Redshift, etc.). Understanding of file formats (Parquet, Avro, ORC) and storage optimization techniques is required.

End-to-End Architecture: Experience designing full-lifecycle data systems, including logging, ingestion (Batch/Stream), quality frameworks, and monitoring.

Technical Leadership: Excellent written and verbal communication skills. Ability to write detailed technical design documents (RFCs) and lead cross-functional technical alignment.

Mentorship & Growth: A strong passion for driving engineering excellence and mentoring engineers. You actively improve team standards for code, testing, and documentation.

---- Preferred Qualifications ----

Technical Strategy & Architecture

1. Batch Processing Optimization: Deep expertise in large-scale Batch Processing systems (Spark, MapReduce, Hive). Proven ability to analyze and refactor inefficient legacy pipelines to reduce latency and resource consumption, while architecting new, highly scalable batch patterns.

2. Streaming & Real-Time: Extensive experience building real-time data platforms using Apache Kafka, Flink, or Spark Streaming, with a focus on state management, windowing, and exactly-once processing semantics.

3. Cloud & Fault Tolerance: Expert hands-on understanding of designing fault-tolerant, multi-datacenter, and cloud-native architectures. Experience with Infrastructure as Code (IaC) (Terraform, Kubernetes) to deploy and manage data infrastructure is highly desirable.

Modern Data Stack & Languages

1. Polyglot Engineering: Proficiency in multiple programming languages (Java, Scala, Go, Python) and deep knowledge of various storage engines (MySQL, Cassandra, Redis, Pinot).

2. Lakehouse Architecture: Experience with modern open table formats like Apache Iceberg, Hudi, or Delta Lake, enabling ACID transactions and time-travel on data lakes.

Observability & Data Trust

1. Advanced Observability & Data Quality: Experience designing end-to-end Data Observability frameworks that go beyond simple logging. Ability to implement automated quality gates ("circuit breakers"), anomaly detection (volume/freshness/schema drift), and SLAs to ensure high-fidelity data for ML and Analytics consumers.

2. Data Governance & Contracts: Passion for defining and enforcing Data Contracts between producers and consumers. Experience establishing governance frameworks that balance agility with strict compliance and security requirements.

Domain & Leadership

1. FinOps & Cost Strategy: Track record of driving cost efficiency in big data environments. Ability to analyze compute/storage spend and re-architect systems to reduce Total Cost of Ownership (TCO) without sacrificing performance.

2. Domain Expertise: Background in Fintech, Payments, or Operations Analytics, with exposure to complex regulatory environments (GDPR, SOX) and data privacy frameworks.

3. Governance & Mentorship: Passion for Data Governance and establishing engineering best practices. A strong history of mentoring senior engineers, driving technical alignment across teams, and balancing long-term technical vision with short-term delivery priorities.

Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuelds progress. What moves us, moves the world - let’s move it forward, together.

Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.

*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to [email protected].